RT info:eu-repo/semantics/article
T1 Neuro-fuzzy optimization of cutting tool geometry in machining using Sugeno and Mamdani inference models
A1 Vicente-García, Luis
A1 Santos Martín, Francisco Javier
A1 Merino Gómez, Elena
A1 San Juan Blanco, Manuel
K1 Neuro-fuzzy systems
K1 ANFIS
K1 Tool geometry optimization
K1 Machining process
K1 3310.05 Ingeniería de Procesos
AB This study presents the design and validation of zero-order Sugeno and Mamdani fuzzy inference systems applied to the estimation of optimal cutting tool angles in machining processes. The input variables considered were the tool destruction energy (D) and the material’s specific cutting energy (U), while the output variables corresponded to the clearance angle (αn), rake angle (γn), and cutting-edge inclination angle (λs). Based on a real dataset of 81 experimental values, a synthetic database of 118,300 records was generated using an adaptive neuro-fuzzy inference system (ANFIS) trained via the backpropagation algorithm, achieving a reliability level of 85%. Both models were implemented in MATLAB using Gaussian membership functions with nine rules per output variable. The Sugeno model employed constant outputs, whereas the Mamdani model used linguistic labels. Validation was performed through the calculation of the cutting-edge angle (βn), derived from αn and γn, by comparing the outputs of both systems. The normalized relative root mean square error (rMSE) was found to be below 6.5%, indicating a high level of agreement between the two models. The results demonstrate that fuzzy inference systems—particularly when integrated with neuro-fuzzy architectures like ANFIS—are effective tools for addressing geometric optimization problems in industrial environments characterized by uncertainty and complexity. It is concluded that this approach provides a robust and accurate alternative for computer-aided cutting tool design.
PB Springer
SN 0268-3768
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/78984
UL https://uvadoc.uva.es/handle/10324/78984
LA eng
NO Vicente-García, L., Santos-Martín, F., Merino-Gómez, E. et al. Neuro-fuzzy optimization of cutting tool geometry in machining using Sugeno and Mamdani inference models. Int J Adv Manuf Technol (2025). https://doi.org/10.1007/s00170-025-16742-x
NO Producción Científica
DS UVaDOC
RD 24-oct-2025